Papers with CG
Read and Comprehend by Gated-Attention Reader with More Belief (N18-4)
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| Challenge: | Existing approaches to read comprehension using gated-attention have been effective . collaborative gating and self-belief aggregation are proposed to address these assumptions . |
| Approach: | They propose to use a document-to-query attention system to gate token encodings of a query . they conjecture that query tokens other than the cloze token may be informative . |
| Outcome: | The proposed approaches advance the state-of-the-art results in CNN, Daily Mail, and Who Did What public test sets. |
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization (2023.findings-emnlp)
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| Challenge: | Recent studies show that sequence-to-sequence (seq2sequ) models struggle with compositional generalization (CG) a crucial property of human language learning is its compositional globalization (GC), the algebraic ability to understand and produce a potentially infinite number of novel combinations from known components. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) extension which learns to compose representations of different encoder layers dynamically for different tasks. |
| Outcome: | The proposed model achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of the proposed model. |
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation (2020.acl-main)
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| Challenge: | Existing word-level policy models that learn dialog policy and language generation from dialog corpora often lead to degeneration issues where the utterances become ungrammatical or repetitive. |
| Approach: | They propose to represent prior dialog transitions as a graph and learn a CG grounded dialog policy that can foster a more coherent and controllable dialog. |
| Outcome: | The proposed framework is able to learn dialog policy in open-domain multi-turn conversation. |
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge (2023.acl-long)
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| Challenge: | Large language models (LLMs) have been studied for their ability to store and utilize positive knowledge. |
| Approach: | They propose to use a constrained keywords-to-sentence generation task and a Boolean question answering task to probe large language models on negative commonsense knowledge. |
| Outcome: | The proposed tasks show that LLMs fail to generate valid sentences grounded in negative commonsense knowledge, yet they can correctly answer yes-or-no questions. |
Development of a General-Purpose Categorial Grammar Treebank (2020.lrec-1)
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| Challenge: | 'general-purpose' categorial grammar treebank is not tailored to specific variants of CG, but rather offers a theory-neutral linguistic resource that can be converted to different versions of 'type-logical grammar' . |
| Approach: | They propose a general-purpose categorial grammar treebank for Japanese that is not tailored to a specific variant of CG but rather offers a theory-neutral resource which can be converted to different versions of GC relatively easily. |
| Outcome: | The proposed treebank improves on the existing Japanese CG treebank on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates). |
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging (2025.acl-long)
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Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
| Challenge: | Current research suggests that multitask training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. |
| Approach: | They employ compositional generalization (CG) to examine the generalization of multimodal large language models in medical imaging. |
| Outcome: | The proposed model can understand unseen medical images and is able to perform CG across classification and detection tasks. |
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality (2022.emnlp-main)
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| Challenge: | Currently, human communication models fail to explicitly model common ground (CG) . less than half of the responses in current data is rated as high quality . |
| Approach: | They propose a dataset that annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |
| Outcome: | The proposed dataset annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |
Localizing Malicious Outputs from CodeLLM (2025.findings-emnlp)
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| Challenge: | Using FreqRank, we localize malicious components in outputs for triggered inputs and their corresponding backdoor triggers. |
| Approach: | They propose a mutation-based defense to localize malicious components in LLM outputs and their corresponding backdoor triggers. |
| Outcome: | The proposed defense has an average attack success rate (ASR) of 86.6% and can localize the backdoor triggers in 98% of cases. |